No version for distro humble showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

No version for distro jazzy showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

No version for distro kilted showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

No version for distro rolling showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

No version for distro galactic showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

No version for distro iron showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

No version for distro melodic showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange

No version for distro noetic showing github. Known supported distros are highlighted in the buttons above.
Package symbol

radar_layer package from radar_layer repo

radar_layer

ROS Distro
github

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description Plugin for Nav2 Costmap 2d to convert radar messages into obstacles on a costmap
Checkout URI https://github.com/polymathrobotics/radar_layer.git
VCS Type git
VCS Version main
Last Updated 2024-11-22
Dev Status UNKNOWN
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

Run-time plugin for Costmap2D radar layer

Additional Links

No additional links.

Maintainers

  • Alexander Yuen

Authors

No additional authors.

Polymath Radar_layer

The radar_layer package is a costmap plugin for Nav2 Costmap 2d to turn radar tracks into data that can be used in a costmap. It is expected the radar track includes the object’s centroid position, velocity, position covariance, velocity covariance, and planar size of the detected object. This package allows the cost to be placed into the costmap in two different ways:

Footprint Stamping

This is the simplest method where a rectangle determined by the x and y size of the obstacle, is placed into the costmap as a lethal obstacle, around the obstacle’s centroid. This placed lethal obstacle can then be inflated with the inflation layer for planning purposes

2D Gaussian Process

The cost is distributed as a 2D gaussian, where the centroid position is the highest cost and the cost surrounding the centroid decreases like a 2D normal distribution, based on the position covariance. To incorporate the size of the obstacle, the position covariance is extended when evaluating the probability density function as follows:

\[\Sigma = \alpha(\begin{bmatrix} \sigma_x & 0 \\ 0 & \sigma_y \end{bmatrix} + \begin{bmatrix} \frac{l}{2} & 0 \\ 0 & \frac{w}{2} \end{bmatrix})\]

with probability density function

\[f = \frac{1}{\sqrt{2\pi|\Sigma|}}\exp{\left(-\frac{1}{2}\begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}^T \Sigma^{-1} \begin{bmatrix} x-\mu_x \\ y-\mu_y \end{bmatrix}\right)}\]

where $\alpha$ is some covariance scaling factor for tuning, $\sigma_x$ and $\sigma_y$ are the position covariances of x and y, respectively, $l$ and $w$ are the length and width of the obstacle respectively, and $\mu_x$ and $\mu_y$ are the means of x and y respectively. Should the radar track also have velocity, the user may choose to project the gaussian distributed costmap into the future in order to plan to avoid a moving object in the future. The original Gaussian’s mean is projected forward with the velocity, and the projected covariance is spread and scaled down as a function of the velocity covariance.

The algorithm used in this package is based on the following work:

@article{guo2023autonomous,
      title={Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties}, 
      author={Hongliang Guo and Zefan Huang and Qiheng Ho and Marcelo Ang and Daniela Rus},
      year={2021},
      journal = {IEEE International Conference on Robotics and Automation}
}

Topics

This costmap layer expects the radar tracks to be published in the ObstacleArray format defined by the navigation2_dynamic package

Configuration

Parameter Description
enabled Whether it is enabled.
combination_method Enum for method to add data to master costmap. Must be 0, 1 or 2, default to 1
observation_sources namespace of sources of data
minimum_probability minimum probability to place in costmap
number_of_time_steps number of time steps to propogate gaussian distribution of obstacle
stamp_footprint Whether to use stamp footprint method or not
sample_time sample time to propogate gaussian distribution of obstacle
<data source>.topic Topic of data
<data source>.datatype Datatype of topic
<data source>.sensor_frame TF frame
<data source>.qos_deadline_hz Sets the QOS deadline on your data source

Example fully-described XML with default parameter values:

``` costmap: costmap: ros__parameters: footprint: “[[-1.0,-0.3],[-1.0,0.3],[3.5,0.3],[3.5,-0.3]]” footprint_padding: 0.0 transform_tolerance: 2.0

  update_frequency: 20.0
  publish_frequency: 10.0

  global_frame: odom
  robot_base_frame: base_link

  width: 100
  height: 100
  origin_x: -50.0
  origin_y: -50.0
  resolution: 0.2
  rolling_window: true

  track_unknown_space: false
  unknown_cost_value: 25
  use_maximum: true

  plugins: ["radar_layer"]

  radar_layer:
    plugin: "radar_layer/RadarLayer"
    enabled: True
    number_of_time_steps: 10
    sample_time: 0.1
    minimum_probability: 0.15

File truncated at 100 lines see the full file

CHANGELOG
No CHANGELOG found.

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

Recent questions tagged radar_layer at Robotics Stack Exchange